Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	138,323
2	South Dakota	136,338
3	Rhode Island	135,685
4	Utah	122,176
5	Tennessee	119,312
6	Arizona	117,017
7	Iowa	113,744
8	Oklahoma	112,356
9	Wisconsin	111,532
10	Nebraska	111,455
11	Arkansas	110,328
12	South Carolina	109,933
13	New Jersey	108,914
14	Alabama	106,298
15	Kansas	105,595
16	Indiana	104,964
17	Mississippi	103,664
18	Idaho	103,331
19	Delaware	102,606
20	Illinois	102,293
21	New York	101,708
22	Nevada	100,548
23	Montana	100,054
24	Florida	100,031
25	Georgia	99,458
26	Wyoming	98,837
27	Kentucky	98,306
28	Texas	98,102
29	Minnesota	97,507
30	Louisiana	97,116
31	Missouri	96,884
32	Massachusetts	96,781
33	California	93,935
34	New Mexico	92,809
35	Connecticut	91,998
36	North Carolina	90,359
37	Ohio	89,665
38	Alaska	89,127
39	Michigan	85,848
40	Pennsylvania	85,816
41	Colorado	84,767
42	West Virginia	82,357
43	Virginia	75,171
44	Maryland	71,452
45	New Hampshire	66,576
46	District of Columbia	65,625
47	Washington	50,421
48	Puerto Rico	47,766
49	Maine	41,625
50	Oregon	41,001
51	Vermont	34,718
52	Hawaii	22,132

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	891
2	Rhode Island	831
3	Pennsylvania	458
4	New Jersey	429
5	Maine	420
6	Florida	350
7	Minnesota	341
8	Delaware	338
9	Colorado	328
10	Connecticut	320
11	New Hampshire	317
12	New York	308
13	Massachusetts	288
14	Illinois	275
15	South Dakota	271
16	Puerto Rico	266
17	North Dakota	250
18	Alaska	248
19	Maryland	224
20	Tennessee	216
21	West Virginia	210
22	North Carolina	206
23	Vermont	199
24	District of Columbia	194
25	Ohio	191
26	Virginia	186
27	Wisconsin	183
28	Montana	179
29	Iowa	177
30	Kentucky	177
31	Indiana	174
32	Nebraska	169
33	South Carolina	167
34	Georgia	159
35	Oregon	157
36	Idaho	153
37	Washington	148
38	Nevada	145
39	Alabama	144
40	Utah	139
41	Texas	134
42	Wyoming	123
43	Missouri	121
44	Louisiana	118
45	Mississippi	112
46	New Mexico	98
47	Arkansas	80
48	Kansas	72
49	Arizona	68
50	Oklahoma	68
51	California	62
52	Hawaii	52

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,818
2	New York	2,617
3	Massachusetts	2,529
4	Rhode Island	2,497
5	Mississippi	2,394
6	Arizona	2,352
7	Connecticut	2,241
8	Louisiana	2,209
9	South Dakota	2,203
10	Alabama	2,189
11	Pennsylvania	2,001
12	North Dakota	1,976
13	Indiana	1,959
14	New Mexico	1,907
15	Illinois	1,885
16	Arkansas	1,884
17	Iowa	1,860
18	South Carolina	1,807
19	Georgia	1,794
20	Michigan	1,782
21	Tennessee	1,748
22	Nevada	1,739
23	Texas	1,710
24	Kansas	1,697
25	Oklahoma	1,692
26	Delaware	1,637
27	Ohio	1,618
28	Florida	1,594
29	West Virginia	1,546
30	District of Columbia	1,544
31	California	1,538
32	Missouri	1,483
33	Kentucky	1,433
34	Montana	1,429
35	Maryland	1,407
36	Wisconsin	1,271
37	Minnesota	1,253
38	Virginia	1,233
39	Wyoming	1,214
40	Nebraska	1,203
41	North Carolina	1,180
42	Idaho	1,128
43	Colorado	1,096
44	New Hampshire	929
45	Washington	710
46	Puerto Rico	684
47	Utah	674
48	Oregon	584
49	Maine	563
50	Alaska	434
51	Vermont	384
52	Hawaii	331

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alaska	8
2	Michigan	8
3	Colorado	6
4	Missouri	5
5	New Jersey	5
6	West Virginia	5
7	Delaware	4
8	Georgia	4
9	Pennsylvania	4
10	Connecticut	3
11	Kentucky	3
12	Mississippi	3
13	New York	3
14	Vermont	3
15	Arkansas	2
16	California	2
17	District of Columbia	2
18	Florida	2
19	Idaho	2
20	Illinois	2
21	Indiana	2
22	Louisiana	2
23	Maryland	2
24	Nevada	2
25	New Mexico	2
26	North Carolina	2
27	Ohio	2
28	Oklahoma	2
29	Puerto Rico	2
30	Rhode Island	2
31	South Carolina	2
32	Texas	2
33	Alabama	1
34	Arizona	1
35	Iowa	1
36	Kansas	1
37	Maine	1
38	Massachusetts	1
39	Minnesota	1
40	New Hampshire	1
41	North Dakota	1
42	Tennessee	1
43	Virginia	1
44	Washington	1
45	Wisconsin	1
46	Wyoming	1
47	Hawaii	0
48	Montana	0
49	Nebraska	0
50	Oregon	0
51	South Dakota	0
52	Utah	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	355,387	1	99
Chattahoochee	Georgia	345,925	2	99
Bent	Colorado	266,631	3	99
Dewey	South Dakota	245,927	4	99
Lincoln	Arkansas	244,856	5	99
Davidson	Tennessee	138,741	194	93
Richland	South Carolina	108,079	1014	67
York	South Carolina	105,239	1143	63
Orange	California	84,587	2109	32
Pierce	Washington	50,123	2893	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,533	1786	43
Davidson	Tennessee	1,331	2025	35
York	South Carolina	1,313	2046	34
Richland	South Carolina	1,292	2078	33
Pierce	Washington	694	2722	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.